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Normalized Hermitian inequality SDPs have equal primal and dual values
Statement
Let and be finite integers. Consider the Hermitian inequality-form primal and dual of Finite semidefinite primal-dual pair, with , , and . Suppose the primal feasible set is nonempty. Then that set is compact, the primal supremum is attained, and the primal and dual values satisfy The dual has a feasible point whose coordinates are all strictly positive and whose matrix slack is positive definite. No strict primal feasibility is required. Dual optimal attainment remains a separate promise: if a dual optimum with , , is supplied, then with the same norm bound.
Facts & Assumptions
Given: The finite Hermitian primal-dual pair, normalization, and primal nonemptiness in the Statement.
Finite semidefinite primal-dual pair and Trace of a product of Hermitian matrices is real: the primal maximizes subject to and ; the dual minimizes subject to and . All these trace quantities are real.
Non-negative and positive operators and A non-negative operator is equivalently self-adjoint with nonnegative eigenvalues, a positive semidefinite matrix in an orthonormal basis, or an operator of the form S^*S: a finite complex Hermitian positive semidefinite matrix has an orthonormal eigenbasis with nonnegative eigenvalues; positive definite means a strictly positive quadratic form on every nonzero vector.
The trace as the sum of the diagonal entries: trace is the diagonal sum, including the empty sum for .
Heine-Borel in : with the Euclidean metric a subset of is compact if and only if it is closed and bounded, and the proof by bisection uses no choice principle; the same holds on the real line: closed bounded subsets of a positive finite-dimensional real coordinate space are compact, without choice.
The image of a compact metric space under a continuous map is compact, and so is the image of any compact subset and A compact subset of a metric space is closed and bounded: a continuous image of a compact metric space is compact and therefore closed in a metric codomain.
A continuous real-valued function on a nonempty compact metric space is bounded and attains a greatest and a least value: a continuous real function on a nonempty compact metric space attains its extrema, without choice.
Interior, closure, boundary, limit point, isolated point and dense subset of a metric space: a point lies in a set's closure exactly when every neighborhood meets the set.
A point outside a nonempty closed convex set is strictly separated from it: a point outside a nonempty closed convex subset of , , has a strict separating real linear functional.
Proof
If , there is one matrix , all traces are zero, and primal nonemptiness implies for every . The primal singleton is compact with optimum , while every is dual feasible and , with equality at . The vector , for is strictly positive and its empty matrix slack is positive definite in the quadratic-form sense, since there is no nonzero vector. Thus all conclusions hold. Henceforth assume .
Regard the Hermitian matrices as a real space with coordinates the real diagonal entries and the real and imaginary parts above the diagonal, giving . Direct diagonal summation yields for Hermitian . This is a real inner product, with norm ; all finite linear maps below are continuous in these coordinates. The cone is convex and closed, being the intersection over of the closed real half-spaces .
Direct summation gives . For , write in an orthonormal eigenbasis, with . Then and , so . For , . Conversely, a Hermitian pairing nonnegatively with every has by testing , so . Thus is self-dual for the real trace pairing. If is Hermitian with , [F4] gives , whence and for .
The first constraint gives for every primal feasible . That feasible set is nonempty, closed by step 1.2 and continuity of its finitely many constraints, and bounded by . Hence [F5] and [F7] give compactness and attainment of the primal maximum. Step 2.1 with gives , so .
Set and for . Step 2.1 gives , so has strictly positive coordinates and positive definite matrix slack. For primal feasible and dual feasible , self-duality gives . Thus , and the dual infimum is finite.
Put and . The image is a nonempty convex cone containing . To prove it closed, fix and the open neighborhood . For every point of and every witness , step 4.1 gives . With , all these witnesses belong to the closed bounded set in . By [F5] and [F6], is compact and closed, and . Every neighborhood of has meeting , so [F8] gives . Thus is closed; no sequence of witness selections was used.
Fix . If with , then is primal feasible and for . Hence . By [F9] there are and with for every . The points for all force . Fix one primal feasible and set ; then . If , that point gives the contradiction . Thus , using no strictness of or its scalar inequalities.
The coordinate rays force . Scaling by arbitrarily large nonnegative scalars forces for every . Self-duality therefore makes dual feasible. Since , separation gives , so . Consequently every exceeds some dual feasible objective. If , taking contradicts the definition of . Thus , and weak duality gives equality. Step 3.1 supplies primal attainment and the interval. An additionally promised optimal dual witness satisfies and retains its promised norm bound.
Remarks
The trace bound and norm normalization establish primal compactness and the search interval directly. Strict dual feasibility closes the product-cone image used for separation. Primal nonemptiness alone ensures a nonzero objective coefficient in that separation. The argument proves no unconditional dual attainment; the source's bound is explicitly a promise about an attained dual optimum.
Depends on
- Finite semidefinite primal-dual pair
- Trace of a product of Hermitian matrices is real
- Non-negative and positive operators
- A non-negative operator is equivalently self-adjoint with nonnegative eigenvalues, a positive semidefinite matrix in an orthonormal basis, or an operator of the form S^*S
- The trace $\operatorname{tr}(A)$ as the sum of the diagonal entries
- The operator norm is zero on the zero domain and otherwise is max_{||v||=1} ||Tv||
- Cauchy–Schwarz: $|\langle u,v\rangle|\leq\lVert u\rVert\lVert v\rVert$, with equality exactly for linearly dependent vectors
- Heine-Borel in $\mathbb{R}^n$: with the Euclidean metric a subset of $\mathbb{R}^n$ is compact if and only if it is closed and bounded, and the proof by bisection uses no choice principle; the same holds on the real line
- The image of a compact metric space under a continuous map is compact, and so is the image of any compact subset
- A compact subset of a metric space is closed and bounded
- A continuous real-valued function on a nonempty compact metric space is bounded and attains a greatest and a least value
- Interior, closure, boundary, limit point, isolated point and dense subset of a metric space
- A point outside a nonempty closed convex set is strictly separated from it
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Sources
- Joran van Apeldoorn et al., Quantum SDP-solvers: Better upper and lower bounds, Section 2 (standard reference, not scraped)